{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hands-on 01: Uso de modelos de propagação para análises sistêmicas\n",
    "\n",
    "### Objetivos\n",
    "As metas desse tutorial são ajudar o usuário na:\n",
    "- Criação de Grid Hexagonal para modelar cobertura de Estações Rádio Base\n",
    "- Análise visual de potência recebida \n",
    "- Análise de Outage de potência\n",
    "- Exercitar programação de front-end para visualização de dados\n",
    "- Criar códigos em Python para o back-end\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prática 01: Sobreamento descorrelacionado\n",
    "\n",
    "Vamos escrever um código para criação do mapa de cobertura (REM) das 7 (sete) estações rádio base (ERBs), como ilustrado na figura a seguir.\n",
    "\n",
    "\n",
    "![fig_hex](../FIGS/HD_01_MATLAB/fig_hex.png)\n",
    "\n",
    "\n",
    "A modelagem tem as seguintes características:\n",
    "\n",
    "- Grid com células hexagonais;\n",
    "- ERBs macrocelulares com altura de 30 m;\n",
    "- Estações móveis com altura média de 1,8 m;\n",
    "- O raio de cada hexágono é um parâmetro ajustável denominado dR;\n",
    "- As dimensões do grid celular com as 7 ERBs é 5dR x 6 $\\sqrt{\\frac{3}{4}}$ dR;\n",
    "- Para fins de definição da Outage (falha da conexão por falta de potência), a sensibilidade do receptor é considerada igual a -104 dBm ([fonte](http://www.comlab.hut.fi/opetus/260/1v153.pdf));\n",
    "- A EIRP (Effective Isotropic Radiated Power) é 57 dBm ([Discussão interessante sobre $P_{TX}$ e EIRP](https://under-linux.org/entry.php?b=1384)). Esse valor é compatível com receptores do GSM ([fonte](https://pt.slideshare.net/naveenjakhar12/gsm-link-budget));\n",
    "- Somente a perda de percurso é considerada como manifestação de canal. Assim, a potência recebida será calculada com o modelo de Okumura-Hata para cidades urbanas grandes;\n",
    "- A frequência da portadora é um parâmetro ajustável denominado dFc;\n",
    "- Para evitar problema numéricos (divisão por zero ou logaritmo de zero), vamos modelar um raio de segurança. Para efeito de cálculo da potência recebida, todos os pontos menores que uma distância denominada dRMin, terão potência recebida igual aquela calculada usando dRMin como distância;\n",
    "- **Sombreamento independente em cada ponto de medição, não importando a distância entre eles.**\n",
    "\n",
    "A ideia é calcular a potência recebida em dBm para pontos equidistantes em toda a ára de cobertura. A distância entre os pontos de medição foi definida como o próximo valor inteiro maior que $\\frac{dR}{20}$. Esse valor pode ser ajustado para melhor visualizar os REMs. \n",
    "\n",
    "**Passo 01:** Crie uma função chamada **fDrawSector.m** com o segunte código. Ela servirá para desenhar um hexágono de centro e raio especificados como parâmetro. Tal função servira para teros certeza que o posicionamento das ERBs estão corretos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created file '/home/labsim/EEC1714/fDrawSector.m'.\n"
     ]
    }
   ],
   "source": [
    "%%file fDrawSector.m\n",
    "function fDrawSector(dR,dCenter)\n",
    "vtHex=zeros(1,0);\n",
    "for ie=1:6\n",
    "    vtHex=[vtHex dR*(cos((ie-1)*pi/3)+j*sin((ie-1)*pi/3))];\n",
    "end\n",
    "vtHex=vtHex+dCenter;\n",
    "vtHexp=[vtHex vtHex(1)];\n",
    "plot(vtHexp,'k');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 02:** Para testar a função, vamos criar um hexágono centrado no ponto (100,50) e com raio 100. Para isso execute o seguinte comando no Matlab (você precisa colocar o arquivo **fDrawSector.m** na pasta de trabalho do Matlab).   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fDrawSector(100,100+50*i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 03:** Crie uma função chamada **fDrawDeploy.m** com o segunte código. Ela servirá para desenhar o grid celular. Tal função servira para teros certeza que o posicionamento das ERBs estão corretos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created file '/home/labsim/EEC1714/fDrawDeploy.m'.\n"
     ]
    }
   ],
   "source": [
    "%%file fDrawDeploy.m\n",
    "function fDrawDeploy(dR,vtBs)\n",
    "% Desenha setores hexagonais\n",
    "hold on;\n",
    "for iBsD = 1 : length(vtBs)\n",
    "    fDrawSector(dR,vtBs(iBsD));\n",
    "end\n",
    "% Plot BSs\n",
    "plot(vtBs,'sk'); axis equal;\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passo 04:** Inspecione e insira o código a seguir no editor do Matlab (salve com o nome **handson2_P2_1.m**). Nesse código, vamos criar um vetor com a posição das 7 ERBs. A posição é ajustada para que a referência, i.e., o ponto (0,0) seja o canto inferior esquerdo do grid. Também já criamos o grid com a dimensão especificada no hands-on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dR = 5e3; % Raio do Hexágono\n",
    "dIntersiteDistance = 2*sqrt(3/4)*dR;                       % Distância entre ERBs (somente para informação)\n",
    "dDimX = 5*dR;                                              % Dimensão X do grid\n",
    "dDimY = 6*sqrt(3/4)*dR;                                    % Dimensão Y do grid\n",
    "% Vetor com posições das BSs (grid Hexagonal com 7 células, uma célula central e uma camada de células ao redor)\n",
    "vtBs = [ 0 ];\n",
    "dOffset = pi/6;\n",
    "for iBs = 2 : 7\n",
    "    vtBs = [ vtBs dR*sqrt(3)*exp( j * ( (iBs-2)*pi/3 + dOffset ) ) ];\n",
    "end\n",
    "vtBs = vtBs + (dDimX/2 + j*dDimY/2);                        % Ajuste de posição das bases (posição relativa ao canto inferior esquerdo)\n",
    "\n",
    "% Desenha setores hexagonais\n",
    "fDrawDeploy(dR,vtBs)\n",
    "axis equal;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prática 02: Cálculo e plot da potência recebida sem e com shadowing\n",
    "\n",
    "Vamos escrever um código para o cálculo da potência recebida nos pontos de medição do REM de cada ERB, e também considerando a composição das 7 ERBs. Como especificado no hands-on, precisamos considerar que a potência recebida de cada ponto de medição é a maior potência recebida em relação às 7 ERBs.\n",
    "\n",
    "**Passo 01:** Inspecione, insira o código a seguir no editor do Matlab (salve com o nome **handson2_p21.m**). Nesse código, vamos:\n",
    "\n",
    "- Criar sete matrizes de distâncias relativas de cada ponto de medição e para cada ERB (matrizes **mtDistEachBs**). Aplicaremos o raio de segurança a essas distâncias;\n",
    "- Com as distâncias, usaremos o modelo de Okumura-Hata para calcular a perda de percurso (matrizes **mtPldB**);\n",
    "- Sortearemos baseado em uma distribiução Lognormal amostras independentes do Sombreamento para cada ponto de medição (matrizes **mtShadowing**);\n",
    "- Com as matrizes de EIRP, da perda de percurso e do sombreamento, calcularemos a potência recebida de cada ERB em cada ponto de medição. Para cada ERB montaremos a matriz **mtPowerEachBSdBm**;\n",
    "- Montaremos uma única matriz **mtPowerFinaldBm** com a maior potência recebida em cada ponto de medição;\n",
    "- Plotaremos o REM de cada ERB e da composição das 7 ERBs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% Entrada de parâmetros\n",
    "dR = 1e3;                                                  % Raio do Hexágono\n",
    "dFc = 800;                                                 % Frequência da portadora\n",
    "dSigmaShad = 8;                                            % Desvio padrão do sombreamento lognormal\n",
    "% Cálculos de outras variáveis que dependem dos parâmetros de entrada\n",
    "dPasso = ceil(dR/20);                                      % Resolução do grid: distância entre pontos de medição\n",
    "dRMin = dPasso;                                            % Raio de segurança\n",
    "dIntersiteDistance = 2*sqrt(3/4)*dR;                       % Distância entre ERBs (somente para informação)\n",
    "dDimX = 5*dR;                                              % Dimensão X do grid\n",
    "dDimY = 6*sqrt(3/4)*dR;                                    % Dimensão Y do grid\n",
    "dPtdBm = 57;                                               % EIRP (incluindo ganho e perdas) (https://pt.slideshare.net/naveenjakhar12/gsm-link-budget)\n",
    "dPtLinear = 10^(dPtdBm/10)*1e-3;                           % EIRP em escala linear\n",
    "dHMob = 5;                                                 % Altura do receptor\n",
    "dHBs = 30;                                                 % Altura do transmissor\n",
    "dAhm = 3.2*(log10(11.75*dHMob)).^2 - 4.97;                 % Modelo Okumura-Hata: Cidade grande e fc  >= 400MHz\n",
    "% Vetor com posições das BSs (grid Hexagonal com 7 células, uma célula central e uma camada de células ao redor)\n",
    "vtBs = [ 0 ];\n",
    "dOffset = pi/6;\n",
    "for iBs = 2 : 7\n",
    "    vtBs = [ vtBs dR*sqrt(3)*exp( j * ( (iBs-2)*pi/3 + dOffset ) ) ];\n",
    "end\n",
    "vtBs = vtBs + (dDimX/2 + j*dDimY/2);                        % Ajuste de posição das bases (posição relativa ao canto inferior esquerdo)\n",
    "%\n",
    "% Matriz de referência com posição de cada ponto do grid (posição relativa ao canto inferior esquerdo)\n",
    "dDimY = ceil(dDimY+mod(dDimY,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "dDimX = ceil(dDimX+mod(dDimX,dPasso));                      % Ajuste de dimensão para medir toda a dimensão do grid\n",
    "[mtPosx,mtPosy] = meshgrid(0:dPasso:dDimX, 0:dPasso:dDimY);\n",
    "% Iniciação da Matriz de com a pontência de recebida máxima em cada ponto\n",
    "% medido. Essa potência é a maior entre as 7 ERBs.\n",
    "mtPowerFinaldBm = -inf*ones(size(mtPosy));\n",
    "mtPowerFinalShaddBm = -inf*ones(size(mtPosy));\n",
    "% Calcular O REM de cada ERB e aculumar a maior potência em cada ponto de medição\n",
    "for iBsD = 1 : length(vtBs)                                 % Loop nas 7 ERBs\n",
    "    % Matriz 3D com os pontos de medição de cada ERB. Os pontos são\n",
    "    % modelados como números complexos X +jY, sendo X a posição na abcissa e Y, a posição no eixo das ordenadas\n",
    "    mtPosEachBS = (mtPosx + j*mtPosy)-(vtBs(iBsD));\n",
    "    mtDistEachBs = abs(mtPosEachBS);              % Distância entre cada ponto de medição e a sua ERB\n",
    "    mtDistEachBs(mtDistEachBs < dRMin) = dRMin;             % Implementação do raio de segurança\n",
    "    % Okumura-Hata (cidade urbana) - dB\n",
    "    mtPldB = 69.55 + 26.16*log10(dFc) + (44.9 - 6.55*log10(dHBs))*log10(mtDistEachBs/1e3) - 13.82*log10(dHBs) - dAhm;\n",
    "    % Shadowing independente em cada ponto\n",
    "    mtShadowing = dSigmaShad*randn(size(mtPosy));\n",
    "    % Potências recebidas em cada ponto de medição sem shadowing\n",
    "    mtPowerEachBSdBm = dPtdBm - mtPldB;           \n",
    "    % Potências recebidas em cada ponto de medição com shadowing\n",
    "    mtPowerEachBSShaddBm = dPtdBm - mtPldB + mtShadowing;           \n",
    "    % Cálulo da maior potência em cada ponto de medição sem shadowing\n",
    "    mtPowerFinaldBm = max(mtPowerFinaldBm,mtPowerEachBSdBm);\n",
    "    % Cálulo da maior potência em cada ponto de medição com shadowing\n",
    "    mtPowerFinalShaddBm = max(mtPowerFinalShaddBm,mtPowerEachBSShaddBm);\n",
    "end\n",
    "% Plot da REM de todo o grid (composição das 7 ERBs) sem shadowing\n",
    "figure;\n",
    "pcolor(mtPosx,mtPosy,mtPowerFinaldBm);\n",
    "colormap(hsv);\n",
    "colorbar;\n",
    "fDrawDeploy(dR,vtBs);\n",
    "axis equal;\n",
    "title(['Todas as 7 ERB sem shadowing']);\n",
    "%\n",
    "% Plot da REM de todo o grid (composição das 7 ERBs) sem shadowing\n",
    "figure;\n",
    "pcolor(mtPosx,mtPosy,mtPowerFinalShaddBm);\n",
    "colormap(hsv);\n",
    "colorbar;\n",
    "fDrawDeploy(dR,vtBs);\n",
    "axis equal;\n",
    "title(['Todas as 7 ERB com shadowing']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A execução do código resulta em:**\n",
    "1. Dois mapas REMs mostrando o grid celular e a potência recebida nos pontos de medição para as 7 ERBS;\n",
    "2. Foi utilizado um colormap diferente da primeria parte do hands-on para diferenciar melhor os níveis de potência e as diferentes cores que o representa;\n",
    "2. A situação com e sem sombreamento estão identificadas no título de cada gráfico;\n",
    "3. Note que a distribuição de potência deixa de ser uniforme e radial quando o sombreamento está presente.\n",
    "\n",
    "**Analise o código com cuidado. Tente compreender a modelagem e a sintaxe usada. Discuta com os colegas. Faça um debug usando a IDE do Matlab.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Entrega 1: Modelagem e avaliação da inclusão de microcélulas\n",
    "\n",
    "\n",
    "Uma solução tecnológica para tratar problemas de cobertura é o uso de repetidores (microcélulas, picocélulas) com intuito de estender o alcance de uma estrutura macrocelular (torres altas e com alta potência). Considere o uso de microcélulas com as seguintes características:\n",
    "\n",
    "  - Potência de transmissão: 0.1 W\n",
    "  - Perda de percurso: PL = 55 + 38 $\\cdot$ log$_{10}$(d)+ (24.5 + $\\frac{1.5*f}{925}$)*log$_{10}$(f), com d em km e f em MHz;\n",
    "   \n",
    "O sistema não é multi-conectividade, i.e., a potência de um ponto NÃO é a SOMA da potência da melhor macrocélula com a microcélula. Assim, como um usuário está conectado ou a macrocélula ou a microcélula, ao final você terá somente uma matriz de potências recebidas.\n",
    "\n",
    "A figura a seguir mostra um exemplo de posicionamento de seis microcélulas. Será que algumas dessas posições são realmente as mais aconselhadas para resolver o problema da Outage?\n",
    "\n",
    "![fig_shadcorr_mod_zoom_300](../FIGS/HD_01_MATLAB/microcelulas.png)\n",
    "\n",
    "Considere -90 dBm como potência mínima de operação, i.e., um usuário (ou ponto no grid) é bloqueado se sua potência recebida for menor que esse valor. Outra consideração importante é que os usuários estão conectados a melhor estação rádio base. Faça os mapas com resolução espacial de 50 m. Se necessário, essa resolução pode ser diminuída, para melhor visualização da cobertura das microcélulas.\n",
    "\n",
    "- Contrua um back-end com métodos para realizar os cálculos necessários:\n",
    "  - Montar o grid celular, considerando somente o grid de pontos que estão dentro dos hexágonos (novo), i.e., escolher uma cor (valor numérico da matriz) que diferencie a região fora dos hexagonos da região dentro; \n",
    "  - Calcular a potência recebida em cada ponto do grid;\n",
    "  - Calcular os pontos que estão em outage e mostrar no grid;\n",
    "  - Calcular os pontos que estão em outage e mostrar a porcentagem de pontos em outage;\n",
    "  - Adicionar microcélulas e recalcular a potência recebida em cada ponto do grid e a outage;\n",
    "- Construa um front-end para visualização dos resultados. O front-end precisa ter:\n",
    "  - Entrada dos parâmetros principais das macrocélulas (sem incluir seu posicionamento);\n",
    "  - Entrada dos parâmetros principais das microcélulas, incluindo sua posição (um pop-up);\n",
    "  - Mapa com a cobertura do sistema celular (somente das macrocélulas);\n",
    "  - Mapa com a cobertura do sistema celular (com macrocélulas e microcélulas);  \n",
    "  - Campo com a outage (em %) para o cenário somente com macrocélulas;\n",
    "  - Campo com a outage (em %) para o cenário com macrocélulas e microcélulas adicionadas;\n",
    "  - Botão para adição de microcélulas;\n",
    "  - Botão para reset dos dados do campo de adição de microcélulas;\n",
    "  - Botão para calcular os resultados (atualização do mapa e dos campos de outage);\n",
    "  - Botão em cada sessão de microcélula par remover microcéula (incluindo os resultados);\n",
    "  - (Extra) Se possível, calcule o posicionamento da microcélula ao clicar no mapa.\n",
    "\n",
    "Com o uso do software, realize a investigação a seguir.\n",
    "\n",
    "Seu alvo é analisar 3 cenários:\n",
    "\n",
    "  - (i) Sistema somente com perda de percurso (sem shadowing), f = 800 MHz, ht = 32m e hr= 1,5m, raio do hexágono 500m;\n",
    "  - (ii) Sistema somente com perda de percurso (sem shadowing), f = 1800 MHz, ht = 32m e hr= 1,5m, raio do hexágono 500m;\n",
    "  - (iii) Sistema somente com perda de percurso (sem shadowing), f = 2100 MHz, ht = 32m e hr= 1,5m, raio do hexágono 500m;\n",
    "  \n",
    "\n",
    "Prepare um primeiro conjunto de resultados, ainda sem microcélulas, e com a potência da macrocélula igual a 21 dBm. Eles são:\n",
    "\n",
    " - REMs identificando a área de Outage no mapa para as três frequências. Analise esses gráficos e decida qual o melhor posicionamento da suas seis microcélulas.\n",
    " \n",
    " - Discuta se as áreas de outage mudaram, e se essa mudança a fez escolher posicionamento diferentes para microcélulas, dependendo da frequência da portadora;\n",
    "\n",
    "De posse do primeiro conjunto de resultados, posicione seis (somente seis) microcélulas nos pontos estratégicos escolhidos por você.\n",
    "\n",
    "Compare os resultados com a situação sem a microcélulas. Quais as principais observações e diferenças?\n",
    "\n",
    "Agora com as microcélulas inteligentemente posicionadas por você, colha os valores de outage com e sem microcélula para cada cenário e os analise.\n",
    " "
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